Prediction of human disease complication incorporating machine learning and local network structures

Abstract Complications have long haunted physicians and patients in clinical medicine. However, the evaluation of complications caused by specific diseases is typically relied on the experience of clinicians or clinical cases. Especially, vast complication diseases involve multiple human body systems, increasing the difficulty of the clinical confirmation. Based on a large scale human disease complication network extracted from the clinical medicine knowledge database, we propose a nonlinear model combined local topological structures and machine learning to explore latent disease-complication relations. As an example, we apply the model to predict unidentified complications of COVID-19 and to detect potential extrapulmonary complications which are significant in the post-pandemic period. Our approach provides an efficient method to identify the candidate complications from the structure of complex network.